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<a href="_ranking_svm_trainer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Support Vector Machine Trainer for the ranking-SVM</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> *</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * \author      T. Glasmachers</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \date        2016</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> *</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * </span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * </span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * </span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * </span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> *</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> */</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span> </div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_RANKINGSVMTRAINER_H</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#define SHARK_ALGORITHMS_RANKINGSVMTRAINER_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_svm_trainer_8h.html">shark/Algorithms/Trainers/AbstractSvmTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_box_constrained_problems_8h.html">shark/Algorithms/QP/BoxConstrainedProblems.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;<a class="code" href="_svm_problems_8h.html">shark/Algorithms/QP/SvmProblems.h</a>&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="preprocessor">#include &lt;<a class="code" href="_qp_solver_8h.html">shark/Algorithms/QP/QpSolver.h</a>&gt;</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="preprocessor">#include &lt;<a class="code" href="_difference_kernel_matrix_8h.html">shark/LinAlg/DifferenceKernelMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="preprocessor">#include &lt;<a class="code" href="_cached_matrix_8h.html">shark/LinAlg/CachedMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="preprocessor">#include &lt;<a class="code" href="_precomputed_matrix_8h.html">shark/LinAlg/PrecomputedMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span> </div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span> </div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment"></span> </div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">///</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">/// \brief Training of an SVM for ranking.</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">///</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">/// A ranking SVM trains a function (linear or linear in a kernel</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// induced feature space, RKHS) with the aim that the function values</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// are consistent with given pairwise rankings. I.e., given are pairs</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// (a, b) of points, and the task of SVM training is to find a</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">/// function f such that f(a) &lt; f(b). More exactly, the hard margin</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">/// ranking SVM aims for f(b) - f(a) &gt;= 1 while minimizing the squared</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">/// RKHS norm of f. The soft-margin SVM relates the constraint analogous</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">/// to a standard C-SVM.</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">///</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">/// The trained model is a real-valued function. To predict the ranking</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">/// of a pair of points the function is applied to both points. The one</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">/// with smaller function value is ranked as being &quot;smaller&quot;, i.e., if f</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">/// is the trained model and a and b are data points, then the following</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">/// code computes the ranking:</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">///</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment">///   bool a_better_than_b = (f(a) &lt; f(b));</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">///</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment">/// \ingroup supervised_trainer</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputType, <span class="keyword">class</span> CacheType = <span class="keywordtype">float</span>&gt;</div>
<div class="foldopen" id="foldopen00072" data-start="{" data-end="};">
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"><a class="line" href="classshark_1_1_ranking_svm_trainer.html">   72</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_ranking_svm_trainer.html" title="Training of an SVM for ranking.">RankingSvmTrainer</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html" title="Super class of all kernelized (non-linear) SVM trainers.">AbstractSvmTrainer</a>&lt; InputType, unsigned int, KernelExpansion&lt;InputType&gt; &gt;</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>{</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html" title="Super class of all kernelized (non-linear) SVM trainers.">AbstractSvmTrainer&lt; InputType, unsigned int, KernelExpansion&lt;InputType&gt;</a> &gt; <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>;</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span> </div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="keyword">public</span>:<span class="comment"></span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">    /// \brief Convenience typedefs:</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment">    /// this and many of the below typedefs build on the class template type CacheType.</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment">    /// Simply changing that one template parameter CacheType thus allows to flexibly</span></div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span><span class="comment">    /// switch between using float or double as type for caching the kernel values.</span></div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="comment">    /// The default is float, offering sufficient accuracy in the vast majority</span></div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span><span class="comment">    /// of cases, at a memory cost of only four bytes. However, the template</span></div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span><span class="comment">    /// parameter makes it easy to use double instead, (e.g., in case high</span></div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span><span class="comment">    /// accuracy training is needed).</span></div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"><a class="line" href="classshark_1_1_ranking_svm_trainer.html#a865d9bea84143e7f5bd16d0ac36dccd6">   86</a></span><span class="comment"></span>    <span class="keyword">typedef</span> CacheType <a class="code hl_typedef" href="classshark_1_1_ranking_svm_trainer.html#a865d9bea84143e7f5bd16d0ac36dccd6" title="Convenience typedefs: this and many of the below typedefs build on the class template type CacheType....">QpFloatType</a>;</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span> </div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"><a class="line" href="classshark_1_1_ranking_svm_trainer.html#af33d5d5d2b50eb37f3b54a80bedc2aab">   88</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_ranking_svm_trainer.html#af33d5d5d2b50eb37f3b54a80bedc2aab">KernelType</a>;</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span><span class="comment"></span> </div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span><span class="comment">    //! Constructor</span></div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span><span class="comment">    //! \param  kernel         kernel function to use for training and prediction</span></div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span><span class="comment">    //! \param  C              regularization parameter - always the &#39;true&#39; value of C, even when unconstrained is set</span></div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span><span class="comment">    //! \param  unconstrained  when a C-value is given via setParameter, should it be piped through the exp-function before using it in the solver?</span></div>
<div class="foldopen" id="foldopen00094" data-start="{" data-end="}">
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno"><a class="line" href="classshark_1_1_ranking_svm_trainer.html#a54df15bbf0fc10b90891b7e47243657a">   94</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_ranking_svm_trainer.html#a54df15bbf0fc10b90891b7e47243657a">RankingSvmTrainer</a>(<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>, <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>)</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>    : <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>(<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>, false, unconstrained)</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>    { }</div>
</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment"></span> </div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00099" data-start="{" data-end="}">
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"><a class="line" href="classshark_1_1_ranking_svm_trainer.html#a25ffc4b3b8a854bdd92386786b67edac">   99</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_ranking_svm_trainer.html#a25ffc4b3b8a854bdd92386786b67edac" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;RankingSvmTrainer&quot;</span>; }</div>
</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span><span class="comment"></span> </div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span><span class="comment">    /// \brief Train the ranking SVM.</span></div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment">    /// This variant of the train function assumes that all pairs of</span></div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">    /// points should be ranked according to the order they appear in</span></div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span><span class="comment">    /// the data set. </span></div>
<div class="foldopen" id="foldopen00107" data-start="{" data-end="}">
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno"><a class="line" href="classshark_1_1_ranking_svm_trainer.html#a2a7c219733a19872f9f340bc2051335b">  107</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_ranking_svm_trainer.html#a2a7c219733a19872f9f340bc2051335b" title="Train the ranking SVM.">train</a>(<a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">KernelExpansion&lt;InputType&gt;</a>&amp; function, <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;InputType&gt;</a> <span class="keyword">const</span>&amp; dataset)</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>    {</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        <span class="comment">// create all pairs</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>        std::size_t n = dataset.<a class="code hl_function" href="group__shark__globals.html#ga814e8b0028cc90dd2af69805e8f8a04d" title="Returns the total number of elements.">numberOfElements</a>();</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        std::vector&lt;std::pair&lt;std::size_t, std::size_t&gt;&gt; pairs;</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;n; i++) {</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>            <span class="keywordflow">for</span> (std::size_t j=0; j&lt;i; j++) {</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>                pairs.push_back(std::make_pair(j, i));</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>            }</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        }</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        <a class="code hl_function" href="classshark_1_1_ranking_svm_trainer.html#a2a7c219733a19872f9f340bc2051335b" title="Train the ranking SVM.">train</a>(function, dataset, pairs);</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>    }</div>
</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment"></span> </div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span><span class="comment">    /// \brief Train the ranking SVM.</span></div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span><span class="comment">    /// This variant of the train function uses integer labels to define</span></div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span><span class="comment">    /// pairwise rankings. It is trained on all pairs of data points</span></div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span><span class="comment">    /// with different label, aiming for a smaller function value for</span></div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span><span class="comment">    /// the point with smaller label.</span></div>
<div class="foldopen" id="foldopen00126" data-start="{" data-end="}">
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno"><a class="line" href="classshark_1_1_ranking_svm_trainer.html#afba6be5a23b22bdd152cc545e6db388e">  126</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_ranking_svm_trainer.html#afba6be5a23b22bdd152cc545e6db388e" title="Train the ranking SVM.">train</a>(<a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">KernelExpansion&lt;InputType&gt;</a>&amp; function, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, unsigned int&gt;</a> <span class="keyword">const</span>&amp; dataset)</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>    {</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        std::vector&lt;std::pair&lt;std::size_t, std::size_t&gt;&gt; pairs;</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>        std::size_t i = 0;</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span>&amp; yi : dataset.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>()) {</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>            std::size_t j = 0;</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> <span class="keyword">const</span>&amp; yj : dataset.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>().<a class="code hl_function" href="group__shark__globals.html#gad9b0233e3adc882ed94f418f80767b09" title="Returns the range of elements.">elements</a>()) {</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>                <span class="keywordflow">if</span> (j &gt;= i) <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>                <span class="keywordflow">if</span> (yi &lt; yj) pairs.push_back(std::make_pair(i, j));</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>                <span class="keywordflow">else</span> <span class="keywordflow">if</span> (yi &gt; yj) pairs.push_back(std::make_pair(j, i));</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>                j++;</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>            }</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>            i++;</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        }</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>        <a class="code hl_function" href="classshark_1_1_ranking_svm_trainer.html#a2a7c219733a19872f9f340bc2051335b" title="Train the ranking SVM.">train</a>(function, dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>(), pairs);</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>    }</div>
</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span><span class="comment"></span> </div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span><span class="comment">    /// \brief Train the ranking SVM.</span></div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span><span class="comment">    ///</span></div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span><span class="comment">    /// This variant of the train function works with explicitly given</span></div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span><span class="comment">    /// pairs of data points. Each pair is identified by the indices of</span></div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span><span class="comment">    /// the training points in the data set.</span></div>
<div class="foldopen" id="foldopen00148" data-start="{" data-end="}">
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"><a class="line" href="classshark_1_1_ranking_svm_trainer.html#a62dc84445c4882ea841c70916cdeb2ef">  148</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_ranking_svm_trainer.html#a62dc84445c4882ea841c70916cdeb2ef" title="Train the ranking SVM.">train</a>(<a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">KernelExpansion&lt;InputType&gt;</a>&amp; function, <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;InputType&gt;</a> <span class="keyword">const</span>&amp; dataset, std::vector&lt;std::pair&lt;std::size_t, std::size_t&gt;&gt; <span class="keyword">const</span>&amp; pairs)</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>    {</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        function.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a38c97766f52bf00e5b0120c46c15f37f">setStructure</a>(<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>, dataset, <span class="keyword">false</span>);</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        <a class="code hl_class" href="classshark_1_1_difference_kernel_matrix.html" title="SVM ranking matrix.">DifferenceKernelMatrix&lt;InputType, QpFloatType&gt;</a> dm(*function.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#aa7f672e5b1a367ce00545f550596bb0c">kernel</a>(), dataset, pairs);</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span> </div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#ae90c5c93fc02fad6fc07ca6b04fc78cc" title="Flag for using a precomputed kernel matrix.">QpConfig::precomputeKernel</a>())</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        {</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>            <a class="code hl_class" href="classshark_1_1_precomputed_matrix.html" title="Precomputed version of a matrix for quadratic programming.">PrecomputedMatrix&lt; DifferenceKernelMatrix&lt;InputType, QpFloatType&gt;</a> &gt; matrix(&amp;dm);</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>            trainInternal(function, dataset, pairs, matrix);</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        }</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>        {</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>            <a class="code hl_class" href="classshark_1_1_cached_matrix.html" title="Efficient quadratic matrix cache.">CachedMatrix&lt; DifferenceKernelMatrix&lt;InputType, QpFloatType&gt;</a> &gt; matrix(&amp;dm);</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>            trainInternal(function, dataset, pairs, matrix);</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>        }</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>    }</div>
</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span> </div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>    <span class="keyword">template</span> &lt;<span class="keyword">typename</span> MatrixType&gt;</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>    <span class="keywordtype">void</span> trainInternal(<a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">KernelExpansion&lt;InputType&gt;</a>&amp; function, <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;InputType&gt;</a> <span class="keyword">const</span>&amp; dataset, std::vector&lt;std::pair&lt;std::size_t, std::size_t&gt;&gt; <span class="keyword">const</span>&amp; pairs, <a class="code hl_typedef" href="_mc_svm_linear_8cpp.html#a88ab98d46276376a56c2a396842cd58e">MatrixType</a>&amp; matrix)</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>    {</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        <a class="code hl_class" href="classshark_1_1_general_quadratic_problem.html" title="Quadratic Problem with only Box-Constraints Let K the kernel matrix, than the problem has the form.">GeneralQuadraticProblem&lt;MatrixType&gt;</a> qp(matrix);</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        qp.linear = RealVector(qp.dimensions(), 1.0);</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        qp.boxMin = RealVector(qp.dimensions(), 0.0);</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        qp.boxMax = RealVector(qp.dimensions(), this-&gt;C());</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_box_constrained_shrinking_problem.html">BoxConstrainedShrinkingProblem&lt; GeneralQuadraticProblem&lt;MatrixType&gt;</a> &gt; ProblemType;</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        ProblemType problem(qp, <a class="code hl_variable" href="classshark_1_1_qp_config.html#ac7bd118550c2bfa50f9497182b4b086d" title="should shrinking be used?">base_type::m_shrinking</a>);</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span> </div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>        <a class="code hl_class" href="classshark_1_1_qp_solver.html" title="Quadratic program solver.">QpSolver&lt;ProblemType&gt;</a> solver(problem);</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>        solver.solve(<a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">base_type::stoppingCondition</a>(), &amp;<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">base_type::solutionProperties</a>());</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        RealVector alpha = problem.getUnpermutedAlpha();</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        RealVector coeff(dataset.<a class="code hl_function" href="group__shark__globals.html#ga814e8b0028cc90dd2af69805e8f8a04d" title="Returns the total number of elements.">numberOfElements</a>(), 0.0);</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(pairs.size() == alpha.size());</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt;alpha.size(); i++)</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        {</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>            <span class="keywordtype">double</span> a = alpha(i);</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>            coeff(pairs[i].first) -= a;</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            coeff(pairs[i].second) += a;</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>        }</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>        blas::column(function.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a3c65dfd17f38eaa461f6400d302fae48">alpha</a>(),0) = coeff;</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span> </div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#a32477b55142b80bd9f82f2a2e201f5b9" title="Flag for sparsifying the model after training.">base_type::sparsify</a>()) function.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a503aaebca6ce5e7d8a6f79e5e039bd9f">sparsify</a>();</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>    }</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>};</div>
</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span> </div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span> </div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>}</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span><span class="preprocessor">#endif</span></div>
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